zoonotic Analysis
Association or Relationships analysis
Laurens
June 01, 2025
Data overview
## # A tibble: 152 × 121
## age sex profession years_experience location has_animal animal_types
## <dbl> <fct> <fct> <dbl> <fct> <fct> <fct>
## 1 35 Female Medical Doctor 6 Tolon Yes Cats, Dog, …
## 2 30 Male Nurse 5 Tolon Yes Dog
## 3 31 Male Nurse 4 Tolon No None
## 4 30 Female Biomedical Sc… 2 Tolon Yes Cat
## 5 36 Female Nurse 2 Tolon No None
## 6 30 Female Nurse 3 Tolon Yes Cat, Dog
## 7 28 Female Nurse 3 Tolon No None
## 8 31 Female Nurse 3 Tolon No None
## 9 26 Female Nurse 1 Tolon No None
## 10 29 Male Nurse 2 Tolon No None
## # ℹ 142 more rows
## # ℹ 114 more variables: has_cat <fct>, has_dog <fct>, has_goat <fct>,
## # has_sheep <fct>, has_cattle <fct>, has_poultry <fct>,
## # zoonosis_definition <fct>, zoonosis_classification <fct>,
## # zoonosis_infectious <fct>, zoonosis_knowledge_total <dbl>,
## # zoonosis_knowledge_cat <fct>, anthrax_prioritized <fct>,
## # rabies_prioritized <fct>, avian_influenza_prioritized <fct>, …## age sex profession years_experience
## Min. :24.00 Female:87 Biomedical Scientist: 8 Min. : 1.000
## 1st Qu.:28.00 Male :65 Medical Doctor : 4 1st Qu.: 2.000
## Median :30.00 Nurse :135 Median : 3.000
## Mean :31.09 Pharmacist : 5 Mean : 3.605
## 3rd Qu.:33.00 3rd Qu.: 4.000
## Max. :51.00 Max. :17.000
##
## location has_animal animal_types has_cat has_dog
## Nyankpala: 31 No :61 None :61 cat : 26 dog : 39
## Tolon :121 Yes:91 Sheep, Goat, Cattle:11 NA's:126 NA's:113
## Cat : 8
## Poultry : 7
## Sheep, Goat : 7
## Dog, Sheep, Goat : 6
## (Other) :52
## has_goat has_sheep has_cattle has_poultry zoonosis_definition
## goat: 52 sheep:60 cattle: 30 Poultry: 21 Correct :63
## NA's:100 NA's :92 NA's :122 NA's :131 Incorrect:89
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## zoonosis_classification zoonosis_infectious zoonosis_knowledge_total
## Correct : 47 Correct :122 Min. :0.000
## Incorrect:105 Incorrect: 30 1st Qu.:1.000
## Median :1.000
## Mean :1.526
## 3rd Qu.:2.000
## Max. :3.000
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## zoonosis_knowledge_cat anthrax_prioritized rabies_prioritized
## high general knowledge:70 Correct :127 Correct :141
## low general knowledge :82 Incorrect: 25 Incorrect: 11
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## avian_influenza_prioritized tb_prioritized ebola_prioritized
## Correct :87 Correct :115 Correct :118
## Incorrect:65 Incorrect: 37 Incorrect: 34
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## lassa_fever_prioritized yellow_fever_prioritized dengue_fever_prioritized
## Correct :101 Correct :107 Correct :87
## Incorrect: 51 Incorrect: 45 Incorrect:65
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## trypanosomiasis_prioritized prioritized_zoonoses_knowledge_total
## Correct :110 Min. :0.000
## Incorrect: 42 1st Qu.:5.000
## Median :7.000
## Mean :6.533
## 3rd Qu.:8.250
## Max. :9.000
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## prioritized_zoonoses_knowledge_cat anthrax_causative_agent
## high knowledge:133 Correct :79
## low knowledge : 19 Incorrect:73
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## anthrax_transmission anthrax_signs anthrax_diagnostic_sample
## Correct :54 Correct :63 Correct :62
## Incorrect:98 Incorrect:89 Incorrect:90
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## anthrax_diagnostic_test anthrax_differential anthrax_treatment_drug
## Correct : 8 Correct : 41 Correct :60
## Incorrect:144 Incorrect:111 Incorrect:92
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## anthrax_vaccination anthrax_knowledge_total anthrax_knowledge_cat
## Correct :92 Min. :0.00 adequate knowledge : 1
## Incorrect:60 1st Qu.:2.00 inadequate knowledge:151
## Median :3.00
## Mean :3.02
## 3rd Qu.:4.00
## Max. :8.00
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## rabies_causative_agent rabies_transmission rabies_signs
## Correct :114 Correct :89 Correct :79
## Incorrect: 38 Incorrect:63 Incorrect:73
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## rabies_diagnostic_sample rabies_diagnostic_test rabies_differential
## Correct :86 Correct : 20 Correct : 46
## Incorrect:66 Incorrect:132 Incorrect:106
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## rabies_treatment_drug rabies_vaccination rabies_knowledge_total
## Correct :61 Correct :116 Min. :0.00
## Incorrect:91 Incorrect: 36 1st Qu.:3.00
## Median :4.00
## Mean :4.02
## 3rd Qu.:5.00
## Max. :8.00
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## rabies_knowledge_cat brucellosis_causative_agent
## adequate knowledge : 2 Correct : 42
## inadequate knowledge:150 Incorrect:110
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## brucellosis_transmission brucellosis_signs brucellosis_diagnostic_sample
## Correct : 28 Correct : 47 Correct : 26
## Incorrect:124 Incorrect:105 Incorrect:126
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## brucellosis_diagnostic_test brucellosis_differential
## Correct : 42 Correct : 48
## Incorrect:110 Incorrect:104
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## brucellosis_treatment_drug brucellosis_vaccination brucellosis_knowledge_total
## Correct : 51 Correct : 29 Min. :0.000
## Incorrect:101 Incorrect:123 1st Qu.:0.000
## Median :2.000
## Mean :2.059
## 3rd Qu.:3.000
## Max. :7.000
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## brucellosis_knowledge_cat trypanosomiasis_causative_agent
## inadequate knowledge:152 Correct :60
## Incorrect:92
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## trypanosomiasis_transmission trypanosomiasis_signs
## Correct :100 Correct : 46
## Incorrect: 52 Incorrect:106
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## trypanosomiasis_diagnostic_sample trypanosomiasis_diagnostic_test
## Correct :61 Correct : 42
## Incorrect:91 Incorrect:110
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## trypanosomiasis_differential trypanosomiasis_treatment_drug
## Correct :57 Correct : 40
## Incorrect:95 Incorrect:112
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## trypanosomiasis_vaccination trypanosomiasis_knowledge_total
## Correct : 42 Min. :0.000
## Incorrect:110 1st Qu.:2.000
## Median :3.000
## Mean :2.947
## 3rd Qu.:4.000
## Max. :7.000
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## trypanosomiasis_knowledge_cat tb_causative_agent tb_transmission
## inadequate knowledge:152 Correct :74 Correct :85
## Incorrect:78 Incorrect:67
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## tb_signs tb_diagnostic_sample tb_diagnostic_test tb_differential
## Correct :83 Correct :106 Correct : 44 Correct : 30
## Incorrect:69 Incorrect: 46 Incorrect:108 Incorrect:122
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## tb_treatment_drug tb_vaccination tb_knowledge_total
## Correct :85 Correct :74 Min. :0.000
## Incorrect:67 Incorrect:78 1st Qu.:2.000
## Median :4.000
## Mean :3.822
## 3rd Qu.:5.000
## Max. :7.000
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## tb_knowledge_cat one_health_definition biodiversity_one_health
## inadequate knowledge:152 Correct :82 Correct :107
## Incorrect:70 Incorrect: 45
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## deforestation_one_health antibiotic_resistance_one_health
## Correct :86 Correct :107
## Incorrect:66 Incorrect: 45
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## armed_conflict_one_health zoonosis_one_health food_safety_one_health
## Correct :62 Correct :113 Correct :116
## Incorrect:90 Incorrect: 39 Incorrect: 36
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## urbanization_one_health animal_husbandry_one_health mental_health_one_health
## Correct :95 Correct :100 Correct :107
## Incorrect:57 Incorrect: 52 Incorrect: 45
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## non_communicable_diseases_one_health animal_welfare_one_health
## Correct :104 Correct :105
## Incorrect: 48 Incorrect: 47
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## climate_change_one_health plant_pests_one_health one_health_knowledge_total
## Correct :106 Correct :106 Min. : 0.000
## Incorrect: 46 Incorrect: 46 1st Qu.: 8.000
## Median :10.000
## Mean : 9.184
## 3rd Qu.:11.000
## Max. :14.000
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## one_health_knowledge_cat zoonosis_workshop zoonosis_collaboration
## high knowledge:120 No :141 No :143
## low knowledge : 32 Yes: 11 Yes: 9
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## collaborated_zoonotic_diseases zoonotic_disease_encounter
## None :143 No :101
## Rabies: 9 Yes: 51
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## encountered_zoonotic_diseases_1 encountered_zoonotic_diseases_2
## Anthrax : 2 None :150
## Ebola : 4 Rabies: 2
## None :101
## Rabies : 41
## Trypanosomiasis: 1
## Tuberculosis : 3
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## zoonoses_program_relevance zoonosis_public_education
## No :68 No :138
## Yes:84 Yes: 14
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## public_educated_zoonotic_diseases one_health_training zoonosis_training
## Ebola : 1 No :120 No :98
## None :138 Yes: 32 Yes:54
## Rabies: 13
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## collab_anthrax encounter_anthrax educate_anthrax collab_rabies
## Min. :0 Min. :0.00000 Min. :0 Min. :0.00000
## 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0.00000
## Median :0 Median :0.00000 Median :0 Median :0.00000
## Mean :0 Mean :0.01316 Mean :0 Mean :0.05921
## 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.:0.00000
## Max. :0 Max. :1.00000 Max. :0 Max. :1.00000
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## encounter_rabies educate_rabies collab_brucellosis encounter_brucellosis
## Min. :0.0000 Min. :0.00000 Min. :0 Min. :0
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0 1st Qu.:0
## Median :0.0000 Median :0.00000 Median :0 Median :0
## Mean :0.2829 Mean :0.08553 Mean :0 Mean :0
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:0 3rd Qu.:0
## Max. :1.0000 Max. :1.00000 Max. :0 Max. :0
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## educate_brucellosis collab_trypanosomiasis encounter_trypanosomiasis
## Min. :0 Min. :0 Min. :0.000000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.000000
## Median :0 Median :0 Median :0.000000
## Mean :0 Mean :0 Mean :0.006579
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.000000
## Max. :0 Max. :0 Max. :1.000000
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## educate_trypanosomiasis collab_tuberculosis encounter_tuberculosis
## Min. :0 Min. :0 Min. :0.00000
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.00000
## Median :0 Median :0 Median :0.00000
## Mean :0 Mean :0 Mean :0.01974
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.00000
## Max. :0 Max. :0 Max. :1.00000
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## educate_tuberculosis
## Min. :0
## 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## | Variable | Biomedical Scientist N = 81 |
Medical Doctor N = 41 |
Nurse N = 1351 |
Pharmacist N = 51 |
p-value2 |
|---|---|---|---|---|---|
| age | 34.9 (8.2) | 42.3 (8.0) | 30.4 (3.4) | 34.4 (4.9) | 0.004 |
| sex | 0.4 | ||||
| Female | 3 (38%) | 1 (25%) | 80 (59%) | 3 (60%) | |
| Male | 5 (63%) | 3 (75%) | 55 (41%) | 2 (40%) | |
| years_experience | 6.0 (6.5) | 4.0 (2.4) | 3.3 (2.4) | 8.2 (4.9) | 0.12 |
| location | 0.003 | ||||
| Nyankpala | 4 (50%) | 3 (75%) | 22 (16%) | 2 (40%) | |
| Tolon | 4 (50%) | 1 (25%) | 113 (84%) | 3 (60%) | |
| has_animal | 6 (75%) | 2 (50%) | 80 (59%) | 3 (60%) | 0.9 |
| 1 Mean (SD); n (%) | |||||
| 2 Kruskal-Wallis rank sum test; Fisher’s exact test | |||||
- Observations
- Demographic Characteristics
Sample Size and Professions: Total observations: 152 health professionals.
Professions: Nurse (135, 88.8%), Biomedical Scientist (8, 5.3%), Pharmacist (5, 3.3%), Medical Doctor (4, 2.6%). The dominance of Nurses limits statistical power for other groups.
Age: Mean age: 31.09 years ( demo_table show variation by profession).
demo_table: Significant differences in age across professions (p=0.004, Kruskal-Wallis):
Biomedical Scientist: 34.9 (SD 8.2)
Medical Doctor: 42.3 (SD 8.0)
Nurse: 30.4 (SD 3.4)
Pharmacist: 34.4 (SD 4.9)
Medical Doctors are older on average, while Nurses are younger.
Sex:
Overall: 87 Females (57.2%), 65 Males (42.8%).
demo_table: Sex distribution varies by profession (p=0.4, Fisher’s exact test, not significant):
Biomedical Scientist: 38% Female, 63% Male
Medical Doctor: 25% Female, 75% Male
Nurse: 59% Female, 41% Male
Pharmacist: 60% Female, 40% Male
Nurses and Pharmacists have a higher proportion of females compared to Medical Doctors and Biomedical Scientists.
Years of Experience:
Mean: 3.61 years (range: 1–17).
demo_table: No significant differences (p=0.12, Kruskal-Wallis):
Biomedical Scientist: 6.0 (SD 6.5)
Medical Doctor: 4.0 (SD 2.4)
Nurse: 3.3 (SD 2.4)
Pharmacist: 8.2 (SD 4.9)
Pharmacists have the highest average experience, but variability is high.
Location:
Two locations: Tolon (121, 79.6%), Nyankpala (31, 20.4%).
demo_table: Significant differences (p=0.003, Fisher’s exact test):
Biomedical Scientist: 50% Tolon, 50% Nyankpala
Medical Doctor: 25% Tolon, 75% Nyankpala
Nurse: 84% Tolon, 16% Nyankpala
Pharmacist: 60% Tolon, 40% Nyankpala
Nurses are predominantly in Tolon, while Medical Doctors are mostly in Nyankpala.
Animal Ownership:
91 (59.9%) own animals, 61 (40.1%) do not.
demo_table: No significant differences (p=0.9, Fisher’s exact test):
Biomedical Scientist: 75% Yes
Medical Doctor: 50% Yes
Nurse: 59% Yes
Pharmacist: 60% Yes
Animal types: Sheep (60), goats (52), dogs (39), cattle (30), cats (26), poultry (21). Many NAs in specific animal variables ( 126 NAs for has_cat).
- General Knowledge of Zoonosis
Zoonosis Knowledge Total (zoonosis_knowledge_total, 0–3):
Mean: 1.53 (median: 1, range: 0–3), moderate knowledge.
Zoonosis Knowledge Category (zoonosis_knowledge_cat):
High general knowledge: 70 (46.1%)
Low general knowledge: 82 (53.9%)
Imbalanced, with slightly more low knowledge.
Components:
zoonosis_definition: 63 Correct (41.4%), 89 Incorrect (58.6%).
zoonosis_classification: 47 Correct (30.9%), 105 Incorrect (69.1%).
zoonosis_infectious: 122 Correct (80.3%), 30 Incorrect (19.7%).
Strongest knowledge in infectious nature, weakest in classification.
- Knowledge of Prioritized Zoonoses in Ghana
Prioritized Zoonoses Knowledge Total (prioritized_zoonoses_knowledge_total, 0–9): Mean: 6.53 (median: 7, range: 0–9), relatively high knowledge.
Prioritized Zoonoses Knowledge Category (prioritized_zoonoses_knowledge_cat): High knowledge: 133 (87.5%)
Low knowledge: 19 (12.5%)
most respondents showing high knowledge.
Specific Diseases: Strong knowledge: Rabies (141 Correct, 92.8%), Anthrax (127, 83.6%), Ebola (118, 77.6%), TB (115, 75.7%), Trypanosomiasis (110, 72.4%), Yellow Fever (107, 70.4%).
Weaker knowledge: Lassa Fever (101, 66.4%), Dengue Fever (87, 57.2%), Avian Influenza (87, 57.2%).
Rabies and Anthrax are best understood, while Avian Influenza and Dengue Fever lag.
- Knowledge of Five Selected Zoonoses (Anthrax, Rabies, Brucellosis, Trypanosomiasis, Tuberculosis)
Anthrax Knowledge:
anthrax_knowledge_total: Mean 3.02 (range: 0–8).
anthrax_knowledge_cat: 151 Inadequate (99.3%), 1 Adequate (0.7%)—nearly invariant.
Strong areas: Vaccination (92 Correct, 60.5%), causative agent (79, 52.0%), signs (63, 41.4%).
Weak areas: Diagnostic test (8 Correct, 5.3%), differential diagnosis (41, 27.0%).
Rabies Knowledge:
rabies_knowledge_total: Mean 4.02 (range: 0–8), highest among the five.
rabies_knowledge_cat: 150 Inadequate (98.7%), 2 Adequate (1.3%)—nearly invariant.
Strong areas: Vaccination (116 Correct, 76.3%), causative agent (114, 75.0%), transmission (89, 58.6%).
Weak areas: Diagnostic test (20 Correct, 13.2%), differential diagnosis (46, 30.3%).
Brucellosis Knowledge:
brucellosis_knowledge_total: Mean 2.06 (range: 0–7), lowest among the five.
brucellosis_knowledge_cat: 152 Inadequate (100%)—completely invariant.
Strong areas: Treatment drug (51 Correct, 33.6%), differential diagnosis (48, 31.6%).
Weak areas: Transmission (28 Correct, 18.4%), diagnostic sample (26, 17.1%), vaccination (29, 19.1%).
Trypanosomiasis Knowledge:
trypanosomiasis_knowledge_total: Mean 2.95 (range: 0–7).
trypanosomiasis_knowledge_cat: 152 Inadequate (100%)—completely invariant.
Strong areas: Transmission (100 Correct, 65.8%), diagnostic sample (61, 40.1%).
Weak areas: Treatment drug (40 Correct, 26.3%), signs (46, 30.3%), vaccination (42, 27.6%).
Tuberculosis Knowledge:
tb_knowledge_total: Mean 3.82 (range: 0–7).
tb_knowledge_cat: 152 Inadequate (100%)—completely invariant.
Strong areas: Diagnostic sample (106 Correct, 69.7%), treatment drug (85, 55.9%), transmission (85, 55.9%).
Weak areas: Differential diagnosis (30 Correct, 19.7%), diagnostic test (44, 28.9%).
Observation: Rabies knowledge is the strongest, while Brucellosis is the weakest.
- Knowledge of One Health and Related Issues
One Health Knowledge Total (one_health_knowledge_total, 0–14):
Mean: 9.18 (median: 10, range: 0–14), indicating strong knowledge.
One Health Knowledge Category (one_health_knowledge_cat):
High knowledge: 120 (78.9%)
Low knowledge: 32 (21.1%)
most respondents showing high knowledge.
Components:
Strong areas: Food safety (116 Correct, 76.3%), zoonosis (113, 74.3%), biodiversity (107, 70.4%), antibiotic resistance (107, 70.4%), mental health (107, 70.4%), plant pests (106, 69.7%), climate change (106, 69.7%), animal welfare (105, 69.1%), non-communicable diseases (104, 68.4%).
Weaker areas: Animal husbandry (100, 65.8%), urbanization (95, 62.5%), deforestation (86, 56.6%), armed conflict (62, 40.8%), definition (82, 53.9%).
Food safety and zoonosis are best understood, while armed conflict and the One Health definition are least understood.
- Experience with Zoonoses and One Health
Zoonosis Workshop (zoonosis_workshop): No: 141 (92.8%), Yes: 11 (7.2%).
Low participation, limiting analysis power.
Zoonosis Collaboration (zoonosis_collaboration): No: 143 (94.1%), Yes: 9 (5.9%).
All collaborations involve Rabies (collaborated_zoonotic_diseases: 143 None, 9 Rabies).
Zoonotic Disease Encounter (zoonotic_disease_encounter): No: 101 (66.4%), Yes: 51 (33.6%).
Encountered diseases (encountered_zoonotic_diseases_1): Rabies (41), Ebola (4), TB (3), Anthrax (2), Trypanosomiasis (1), None (101).
encountered_zoonotic_diseases_2: Mostly None (150), with 2 Rabies cases.
Rabies is the most encountered zoonotic disease.
Zoonosis Public Education (zoonosis_public_education): No: 138 (90.8%), Yes: 14 (9.2%).
Educated diseases (public_educated_zoonotic_diseases): None (138), Rabies (13), Ebola (1).
One Health Training (one_health_training): No: 120 (78.9%), Yes: 32 (21.1%).
Zoonosis Training (zoonosis_training): No: 98 (64.5%), Yes: 54 (35.5%).
Observation: Engagement in zoonosis-related activities (workshops, collaboration, education) is low, with Rabies dominating encounters and education efforts. Training in One Health and zoonosis is more common but still limited.
- Binary Disease Indicators
Collaboration: collab_rabies: 9 Yes (5.9%), 143 No (94.1%).
Others (collab_anthrax, collab_brucellosis, collab_trypanosomiasis, collab_tuberculosis): All 0, unsuitable for analysis.
Encounters:
encounter_rabies: 43 Yes (28.3%), 109 No (71.7%).
encounter_anthrax: 2 Yes (1.3%), 150 No (98.7%).
encounter_tuberculosis: 3 Yes (2.0%), 149 No (98.0%).
encounter_trypanosomiasis: 1 Yes (0.7%), 151 No (99.3%).
encounter_brucellosis: All 0.
Public Education:
educate_rabies: 13 Yes (8.6%), 139 No (91.4%).
Others (educate_anthrax, educate_brucellosis, educate_trypanosomiasis, educate_tuberculosis): All 0.
Observation: Rabies dominates collaboration, encounters, and public education. Other diseases have extremely low prevalence, limiting their analysis.
- Summary
The dataset reveals a predominantly Nurse-based sample (88.8%) with moderate general zoonosis knowledge, strong knowledge of prioritized zoonoses (especially Rabies and Anthrax), and robust One Health knowledge (mean 9.18/14). Rabies dominates zoonotic disease encounters (28.3%), collaboration (5.9%), and public education (8.6%), while other diseases (e.g., Brucellosis, Anthrax) have negligible engagement. Knowledge of Brucellosis is the weakest among the five zoonoses, and individual disease knowledge categories are nearly invariant, necessitating numeric or composite scores for analysis. Low engagement in workshops (7.2%), collaboration (5.9%), and public education (9.2%) highlights gaps in zoonosis-related activities. The small sample sizes for Medical Doctors and Pharmacists, combined with imbalanced categorical variables and low-prevalence binary outcomes, pose challenges for detecting significant associations with profession. The significant differences in age and location by profession suggest potential confounders for the planned analyses.
Relationships
| 0 | 1 | 2 | 3 | |
|---|---|---|---|---|
| Biomedical Scientist | 0 | 5 | 2 | 1 |
| Medical Doctor | 0 | 2 | 2 | 0 |
| Nurse | 13 | 59 | 40 | 23 |
| Pharmacist | 2 | 1 | 1 | 1 |
## Df Sum Sq Mean Sq F value Pr(>F)
## profession 3 0.57 0.1896 0.239 0.869
## Residuals 148 117.33 0.7927
##
## high general knowledge low general knowledge
## Biomedical Scientist 3 5
## Medical Doctor 2 2
## Nurse 63 72
## Pharmacist 2 3##
## high general knowledge low general knowledge
## Biomedical Scientist 37.50000 62.50000
## Medical Doctor 50.00000 50.00000
## Nurse 46.66667 53.33333
## Pharmacist 40.00000 60.00000##
## Pearson's Chi-squared test
##
## data: prof_zoon_know
## X-squared = 0.35484, df = 3, p-value = 0.9494| high general knowledge | low general knowledge | high general knowledge | low general knowledge | |
|---|---|---|---|---|
| Biomedical Scientist | 3 | 5 | 37.50000 | 62.50000 |
| Medical Doctor | 2 | 2 | 50.00000 | 50.00000 |
| Nurse | 63 | 72 | 46.66667 | 53.33333 |
| Pharmacist | 2 | 3 | 40.00000 | 60.00000 |
| Note: | ||||
| Chi-square test: X² = 0.35 , df = 3 , p-value = 0.949 |
| high general knowledge | low general knowledge | |
|---|---|---|
| Biomedical Scientist | 3 | 5 |
| Medical Doctor | 2 | 2 |
| Nurse | 63 | 72 |
| Pharmacist | 2 | 3 |
##
## Fisher's Exact Test for Count Data
##
## data: table_test
## p-value = 0.9651
## alternative hypothesis: two.sided##
## high knowledge low knowledge
## Biomedical Scientist 7 1
## Medical Doctor 4 0
## Nurse 118 17
## Pharmacist 4 1##
## high knowledge low knowledge
## Biomedical Scientist 87.50000 12.50000
## Medical Doctor 100.00000 0.00000
## Nurse 87.40741 12.59259
## Pharmacist 80.00000 20.00000##
## Fisher's Exact Test for Count Data
##
## data: prof_prior_zoon
## p-value = 0.7932
## alternative hypothesis: two.sided| high knowledge | low knowledge | high knowledge | low knowledge | |
|---|---|---|---|---|
| Biomedical Scientist | 7 | 1 | 87.50000 | 12.50000 |
| Medical Doctor | 4 | 0 | 100.00000 | 0.00000 |
| Nurse | 118 | 17 | 87.40741 | 12.59259 |
| Pharmacist | 4 | 1 | 80.00000 | 20.00000 |
| Note: | ||||
| Fisher’s exact test: p-value = 0.793 |
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Biomedical Scientist | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 4 |
| Medical Doctor | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 |
| Nurse | 6 | 1 | 2 | 1 | 7 | 20 | 32 | 14 | 19 | 33 |
| Pharmacist | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 1 | 0 |
## Df Sum Sq Mean Sq F value Pr(>F)
## profession 3 21.1 7.019 1.399 0.246
## Residuals 148 742.8 5.019
- Observations
- Profession vs. General Knowledge of Zoonosis (zoonosis_knowledge_total)
Cross-tabulation (Counts):
Nurses have the highest counts across knowledge scores (0–3), reflecting their large sample size (135).
Biomedical Scientists: Most scores are 2 (4/8) or 3 (1/8), with 5/8 scoring ≥2.
Medical Doctors: Evenly distributed (1 each for 0, 2, 3; 0 for 1).
Pharmacists: Most scores are 1 (2/5) or 2 (2/5).
Nurses: Scores spread across 0 (25), 1 (59), 2 (40), 3 (14), with a peak at 1.
ANOVA Test: F(3, 148) = 0.239, p = 0.869.
No significant differences in general zoonosis knowledge scores across professions.
The low F-value and high p-value suggest minimal variation in knowledge scores attributable to profession.
Visualization (Boxplot): Boxplots show overlapping distributions across professions.
Median scores appear similar (around 1–2), with Nurses showing the widest spread (0–3) due to their sample size.
Biomedical Scientists and Medical Doctors have slightly higher medians, but small sample sizes (8 and 4) limit conclusions.
Observation: General zoonosis knowledge (mean 1.53 from summary) is moderate but does not vary significantly by profession.
The small sample sizes for Medical Doctors and Pharmacists reduce power to detect differences.
Nurses’ knowledge is variable, likely due to their large sample size capturing more diversity.
- Profession vs. Level of Knowledge on Zoonosis (zoonosis_knowledge_cat)
Cross-tabulation (Counts):
Biomedical Scientist: 3 high (37.5%), 5 low (62.5%).
Medical Doctor: 2 high (50%), 2 low (50%).
Nurse: 63 high (46.7%), 72 low (53.3%).
Pharmacist: 2 high (40%), 3 low (60%).
Nurses dominate counts (63 high, 72 low), but proportions are similar across professions.
Proportions: High knowledge proportions: Medical Doctors (50%), Nurses (46.7%), Pharmacists (40%), Biomedical Scientists (37.5%).
Low knowledge is slightly more common across all groups, especially for Biomedical Scientists (62.5%).
Statistical Test: Fisher’s exact test: p = 0.9651 (used due to expected cell counts < 5).
Chi-squared test (for reference): X² = 0.35484, df = 3, p = 0.9494.
No significant association between profession and level of zoonosis knowledge.
The high p-value indicates that the distribution of high/low knowledge is similar across professions.
Visualization (Stacked Bar Plot): Stacked bar plots show similar proportions of high/low knowledge across professions.
Medical Doctors have the highest proportion of high knowledge (50%), but the small sample size (4) limits reliability.
Nurses show a balanced split (46.7% high, 53.3% low), reflecting their large sample size.
Observation: The level of zoonosis knowledge (high vs. low) is not significantly associated with profession.
The near-even split (70 high, 82 low overall from summary) and lack of significant differences suggest uniform knowledge gaps across professions.
Small sample sizes for non-Nurse professions and low expected counts necessitate Fisher’s test, reducing power.
- Profession vs. Knowledge of Prioritized Zoonoses in Ghana (prioritized_zoonoses_knowledge_total)
Cross-tabulation (Counts):
Biomedical Scientist: Scores range from 2 (1) to 9 (4), with most at 8–9 (5/8).
Medical Doctor: Scores at 7 (1), 8 (2), 9 (1), all ≥7.
Nurse: Scores range from 0 (6) to 9 (33), with peaks at 6 (32) and 9 (33).
Pharmacist: Scores at 3 (1), 5 (2), 6 (1), 8 (1), spread across lower values.
Nurses show the broadest distribution, while Medical Doctors have consistently high scores.
ANOVA Test: F(3, 148) = 1.399, p = 0.246.
No significant differences in prioritized zoonoses knowledge scores across professions.
The moderate F-value suggests some variation, but the p-value indicates it’s not statistically significant.
Visualization (Boxplot): Boxplots show Medical Doctors with the highest median score (around 8), followed by Biomedical Scientists (around 7–8).
Nurses have a wide spread (0–9), with a median around 7.
Pharmacists have a lower median (around 5–6) and narrower range.
Overlap in distributions suggests no clear profession-based differences.
Observation: Knowledge of prioritized zoonoses (mean 6.53 from summary) is relatively high but does not vary significantly by profession.
Medical Doctors and Biomedical Scientists tend toward higher scores, but small sample sizes (4 and 8) limit conclusions.
Nurses’ wide score range reflects their large sample size, capturing more variability.
- Profession vs. Knowledge of Prioritized Zoonoses in Ghana (prioritized_zoonoses_knowledge_cat)
Cross-tabulation (Counts): Biomedical Scientist: 7 high (87.5%), 1 low (12.5%).
Medical Doctor: 4 high (100%), 0 low (0%).
Nurse: 118 high (87.4%), 17 low (12.6%).
Pharmacist: 4 high (80%), 1 low (20%).
Nurses dominate counts (118 high, 17 low), but proportions are similar except for Medical Doctors (all high).
Proportions: High knowledge proportions: Medical Doctors (100%), Biomedical Scientists (87.5%), Nurses (87.4%), Pharmacists (80%).
Low knowledge is rare, especially for Medical Doctors (0%) and Biomedical Scientists (12.5%).
Fisher’s Exact Test: p = 0.7932.
No significant association between profession and level of prioritized zoonoses knowledge.
The high p-value indicates similar distributions of high/low knowledge across professions.
Visualization (Stacked Bar Plot): Stacked bar plots show high knowledge dominating across all professions.
Medical Doctors have 100% high knowledge, but the small sample size (4) limits reliability.
Nurses and Biomedical Scientists have nearly identical proportions (87.4% and 87.5% high).
Pharmacists have a slightly higher low-knowledge proportion (20%).
Observation: The level of prioritized zoonoses knowledge is overwhelmingly high (133 high, 19 low overall from summary) and not significantly associated with profession.
Medical Doctors show perfect high knowledge, but the small sample size reduces confidence.
##
## Fisher's Exact Test for Count Data
##
## data: prof_anthrax
## p-value = 1
## alternative hypothesis: two.sided| adequate knowledge | inadequate knowledge | adequate knowledge | inadequate knowledge | |
|---|---|---|---|---|
| Biomedical Scientist | 0 | 8 | 0.0000000 | 100.00000 |
| Medical Doctor | 0 | 4 | 0.0000000 | 100.00000 |
| Nurse | 1 | 134 | 0.7407407 | 99.25926 |
| Pharmacist | 0 | 5 | 0.0000000 | 100.00000 |
| Note: | ||||
| Fisher’s exact test: p-value = 1 |
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
|---|---|---|---|---|---|---|---|---|---|
| Biomedical Scientist | 1 | 2 | 0 | 4 | 0 | 1 | 0 | 0 | 0 |
| Medical Doctor | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 |
| Nurse | 13 | 16 | 23 | 33 | 22 | 16 | 8 | 3 | 1 |
| Pharmacist | 0 | 0 | 0 | 2 | 1 | 2 | 0 | 0 | 0 |
## Df Sum Sq Mean Sq F value Pr(>F)
## profession 3 9.1 3.024 0.945 0.421
## Residuals 148 473.9 3.202
| adequate knowledge | inadequate knowledge | |
|---|---|---|
| Biomedical Scientist | 0 | 8 |
| Medical Doctor | 0 | 4 |
| Nurse | 2 | 133 |
| Pharmacist | 0 | 5 |
| adequate knowledge | inadequate knowledge | |
|---|---|---|
| Biomedical Scientist | 0.000000 | 100.00000 |
| Medical Doctor | 0.000000 | 100.00000 |
| Nurse | 1.481481 | 98.51852 |
| Pharmacist | 0.000000 | 100.00000 |
##
## Fisher's Exact Test for Count Data
##
## data: prof_rabies_cat
## p-value = 1
## alternative hypothesis: two.sided
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
|---|---|---|---|---|---|---|---|---|---|
| Biomedical Scientist | 0 | 0 | 2 | 2 | 3 | 0 | 1 | 0 | 0 |
| Medical Doctor | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 0 |
| Nurse | 5 | 5 | 8 | 31 | 35 | 25 | 21 | 3 | 2 |
| Pharmacist | 0 | 0 | 1 | 0 | 2 | 1 | 1 | 0 | 0 |
## Df Sum Sq Mean Sq F value Pr(>F)
## profession 3 6.2 2.057 0.783 0.505
## Residuals 148 388.8 2.627
| inadequate knowledge | |
|---|---|
| Biomedical Scientist | 8 |
| Medical Doctor | 4 |
| Nurse | 135 |
| Pharmacist | 5 |
| Note: | |
| All respondents had ‘inadequate knowledge’ - no statistical test possible |
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|---|---|---|---|---|---|---|---|
| Biomedical Scientist | 2 | 0 | 1 | 1 | 2 | 1 | 1 | 0 |
| Medical Doctor | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| Nurse | 45 | 23 | 19 | 20 | 13 | 7 | 5 | 3 |
| Pharmacist | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 |
## Df Sum Sq Mean Sq F value Pr(>F)
## profession 3 34.2 11.388 3.019 0.0318 *
## Residuals 148 558.3 3.772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
| inadequate knowledge | |
|---|---|
| Biomedical Scientist | 8 |
| Medical Doctor | 4 |
| Nurse | 135 |
| Pharmacist | 5 |
| Note: | |
| All respondents had ‘inadequate knowledge’ - no statistical test possible |
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|---|---|---|---|---|---|---|---|
| Biomedical Scientist | 0 | 0 | 1 | 0 | 2 | 4 | 1 | 0 |
| Medical Doctor | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 |
| Nurse | 18 | 16 | 28 | 22 | 24 | 19 | 8 | 0 |
| Pharmacist | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 0 |
## Df Sum Sq Mean Sq F value Pr(>F)
## profession 3 35.8 11.945 3.862 0.0107 *
## Residuals 148 457.7 3.093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
| inadequate knowledge | |
|---|---|
| Biomedical Scientist | 8 |
| Medical Doctor | 4 |
| Nurse | 135 |
| Pharmacist | 5 |
| Note: | |
| All respondents had ‘inadequate knowledge’ - no statistical test possible |
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|---|---|---|---|---|---|---|---|
| Biomedical Scientist | 0 | 0 | 0 | 2 | 1 | 4 | 0 | 1 |
| Medical Doctor | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 |
| Nurse | 14 | 11 | 15 | 22 | 18 | 24 | 15 | 16 |
| Pharmacist | 0 | 1 | 0 | 0 | 2 | 2 | 0 | 0 |
## Df Sum Sq Mean Sq F value Pr(>F)
## profession 3 25.8 8.599 1.975 0.12
## Residuals 148 644.4 4.354
- Observations
- Profession vs. Anthrax Knowledge
Categorical Outcome (anthrax_knowledge_cat, adequate/inadequate):
Cross-tabulation (Counts and Proportions):
Biomedical Scientist: 0 adequate, 8 inadequate (100% inadequate).
Medical Doctor: 0 adequate, 4 inadequate (100% inadequate).
Nurse: 1 adequate (0.7%), 134 inadequate (99.3%).
Pharmacist: 0 adequate, 5 inadequate (100% inadequate).
Nearly invariant: Only 1 Nurse has adequate knowledge, making statistical analysis unreliable.
Fisher’s Exact Test: p = 1.0.
No significant association between profession and anthrax knowledge level.
The invariant outcome (151/152 inadequate) and small non-Nurse sample sizes cabnt make meaningful differences.
Visualization (Stacked Bar Plot): All professions show nearly 100% inadequate knowledgeno.
The single adequate case (Nurse) is barely visible
Numeric Outcome (anthrax_knowledge_total, 0–8):
Cross-tabulation (Counts):
Biomedical Scientist: Scores range from 0 (1) to 5 (1), with a peak at 3 (4/8).
Medical Doctor: Scores at 0 (1), 4 (1), 5 (2), relatively high.
Nurse: Scores range from 0 (13) to 8 (1), with peaks at 3 (33) and 4 (22).
Pharmacist: Scores at 3 (2), 4 (1), 5 (2), mid-range.
Nurses show the broadest distribution due to their large sample size.
ANOVA Test: F(3, 148) = 0.945, p = 0.421.
No significant differences in anthrax knowledge scores across professions.
The low F-value suggests minimal variation by profession.
Visualization (Boxplot): Medians: Medical Doctors and Biomedical Scientists around 4–5, Nurses and Pharmacists around 3.
Nurses have the widest spread (0–8), reflecting sample size.
Overlapping distributions indicate no clear profession-based differences.
Observation: Anthrax knowledge is generally low (mean 3.02/8 from summary), with nearly all respondents classified as inadequate (151/152).
No significant profession-based differences in either categorical or numeric outcomes.
- Profession vs. Rabies Knowledge
Categorical Outcome (rabies_knowledge_cat, adequate/inadequate):
Cross-tabulation (Counts and Proportions):
Biomedical Scientist: 0 adequate, 8 inadequate (100% inadequate).
Medical Doctor: 0 adequate, 4 inadequate (100% inadequate).
Nurse: 2 adequate (1.5%), 133 inadequate (98.5%).
Pharmacist: 0 adequate, 5 inadequate (100% inadequate).
Nearly invariant: Only 2 Nurses have adequate knowledge.
Fisher’s Exact Test: p = 1.0.
No significant association between profession and rabies knowledge level.
The sparse adequate cases (2/152) and small non-Nurse groups limit analysis.
Visualization (Stacked Bar Plot): All professions show nearly 100% inadequate knowledge.
The two adequate cases (Nurses) are minimally visible.
Numeric Outcome (rabies_knowledge_total, 0–8):
Cross-tabulation (Counts):
Biomedical Scientist: Scores range from 2 (2) to 6 (1), with a peak at 4 (3/8).
Medical Doctor: Scores at 2 (1), 6 (3), relatively high.
Nurse: Scores range from 0 (5) to 8 (2), with peaks at 4 (35) and 5 (25).
Pharmacist: Scores at 2 (1), 4 (2), 5 (1), 6 (1), mid-range.
ANOVA Test: F(3, 148) = 0.783, p = 0.505.
No significant differences in rabies knowledge scores across professions.
Visualization (Boxplot): Medians: Medical Doctors around 6, others around 4.
Nurses show the widest spread (0–8).
Overlapping distributions suggest no clear differences.
Observation: Rabies knowledge is the highest among the five zoonoses (mean 4.02/8), but still predominantly inadequate (150/152).
No significant profession-based differences.
- Profession vs. Brucellosis Knowledge
Categorical Outcome (brucellosis_knowledge_cat, inadequate):
Cross-tabulation:
All professions: 100% inadequate (8 Biomedical Scientists, 4 Medical Doctors, 135 Nurses, 5 Pharmacists).
Completely invariant: No adequate knowledge cases.
Statistical Test: No test possible due to zero variation.
Visualization (Stacked Bar Plot): Uniformly 100% inadequate across all professions.
Numeric Outcome (brucellosis_knowledge_total, 0–7):
Cross-tabulation (Counts):
Biomedical Scientist: Scores range from 0 (2) to 6 (1), spread across 0–6.
Medical Doctor: Scores at 0 (1), 5 (1), 6 (1), 7 (1), high.
Nurse: Scores range from 0 (45) to 7 (3), with a peak at 0 (45).
Pharmacist: Scores at 1 (1), 2 (1), 3 (3), low to mid-range.
ANOVA Test: F(3, 148) = 3.019, p = 0.0318 (significant at α = 0.05).
Significant differences in brucellosis knowledge scores across professions.
The first significant result among the zoonoses analyses.
Visualization (Boxplot): Medians: Medical Doctors highest (around 5–6), followed by Biomedical Scientists (around 3–4), Nurses and Pharmacists lowest (around 1–2).
Nurses have a wide spread (0–7), Pharmacists are concentrated at lower scores.
Observation: Brucellosis knowledge is the lowest (mean 2.06/7), with all respondents classified as inadequate.
The significant ANOVA result suggests profession-based differences in numeric scores, with Medical Doctors scoring highest, followed by Biomedical Scientists.
- Profession vs. Trypanosomiasis Knowledge
Categorical Outcome (trypanosomiasis_knowledge_cat, inadequate):
Cross-tabulation:
All professions: 100% inadequate (8 Biomedical Scientists, 4 Medical Doctors, 135 Nurses, 5 Pharmacists).
Completely invariant.
Statistical Test: No test possible.
Visualization (Stacked Bar Plot): Uniformly 100% inadequate.
Numeric Outcome (trypanosomiasis_knowledge_total, 0–7):
Cross-tabulation (Counts):
Biomedical Scientist: Scores range from 2 (1) to 6 (1), with a peak at 5 (4/8).
Medical Doctor: Scores at 1 (1), 5 (1), 6 (1), 7 (1), high.
Nurse: Scores range from 0 (18) to 6 (8), with peaks at 2 (28) and 3 (22).
Pharmacist: Scores at 1 (1), 3 (1), 4 (3), low to mid-range.
ANOVA Test: F(3, 148) = 3.862, p = 0.0107 (significant at α = 0.05).
Significant differences in trypanosomiasis knowledge scores across professions.
Visualization (Boxplot): Medians: Medical Doctors and Biomedical Scientists highest (around 5–6), Nurses and Pharmacists lower (around 3).
Nurses show a wide spread (0–6).
Observation: Trypanosomiasis knowledge is moderate (mean 2.95/7), but all respondents are inadequate.
Significant profession-based differences in numeric scores, with Medical Doctors and Biomedical Scientists scoring higher.
- Profession vs. Tuberculosis (TB) Knowledge
Categorical Outcome (tb_knowledge_cat, inadequate):
Cross-tabulation:
All professions: 100% inadequate (8 Biomedical Scientists, 4 Medical Doctors, 135 Nurses, 5 Pharmacists).
Completely invariant.
Statistical Test: No test possible.
Visualization (Stacked Bar Plot): Uniformly 100% inadequate.
Numeric Outcome (tb_knowledge_total, 0–7):
Cross-tabulation (Counts):
Biomedical Scientist: Scores range from 3 (2) to 7 (1), with a peak at 5 (4/8).
Medical Doctor: All scores at 6 (4/4), consistently high.
Nurse: Scores range from 0 (14) to 7 (16), with peaks at 3 (22) and 5 (24).
Pharmacist: Scores at 1 (1), 4 (2), 5 (2), mid-range.
ANOVA Test: F(3, 148) = 1.975, p = 0.12.
No significant differences in TB knowledge scores across professions.
Visualization (Boxplot): Medians: Medical Doctors highest (6), Biomedical Scientists around 5, Nurses and Pharmacists around 3–4.
Nurses have the widest spread (0–7).
Observation: TB knowledge is relatively strong (mean 3.82/7), but all respondents are inadequate.
No significant profession-based differences, though Medical Doctors consistently score high (all 6).
| high knowledge | low knowledge | |
|---|---|---|
| Biomedical Scientist | 5 | 3 |
| Medical Doctor | 4 | 0 |
| Nurse | 106 | 29 |
| Pharmacist | 5 | 0 |
| high knowledge | low knowledge | |
|---|---|---|
| Biomedical Scientist | 62.50000 | 37.50000 |
| Medical Doctor | 100.00000 | 0.00000 |
| Nurse | 78.51852 | 21.48148 |
| Pharmacist | 100.00000 | 0.00000 |
##
## Fisher's exact test: p-value = 0.357| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Biomedical Scientist | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 1 |
| Medical Doctor | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 2 |
| Nurse | 7 | 1 | 3 | 4 | 6 | 8 | 4 | 9 | 17 | 28 | 19 | 9 | 14 | 6 |
| Pharmacist | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 1 | 0 | 0 |
## profession one_health_knowledge_total.mean
## 1 Biomedical Scientist 9.250000
## 2 Medical Doctor 12.500000
## 3 Nurse 9.044444
## 4 Pharmacist 10.200000
## one_health_knowledge_total.sd one_health_knowledge_total.n
## 1 4.803273 8.000000
## 2 2.380476 4.000000
## 3 3.429170 135.000000
## 4 1.923538 5.000000| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| profession | 3 | 51.80877 | 17.26959 | 1.4448 | 0.232143 |
| Residuals | 148 | 1769.03333 | 11.95293 | NA | NA |
##
##
## ## Analysis for: one_health_definition##
## Fisher's exact test: p-value = 0.296
##
##
## ## Analysis for: biodiversity_one_health##
## Fisher's exact test: p-value = 0.331
##
##
## ## Analysis for: deforestation_one_health##
## Fisher's exact test: p-value = 0.673
##
##
## ## Analysis for: antibiotic_resistance_one_health##
## Fisher's exact test: p-value = 0.678
##
##
## ## Analysis for: armed_conflict_one_health##
## Fisher's exact test: p-value = 0.006
##
##
## ## Analysis for: zoonosis_one_health##
## Fisher's exact test: p-value = 0.363
##
##
## ## Analysis for: food_safety_one_health##
## Fisher's exact test: p-value = 0.471
##
##
## ## Analysis for: urbanization_one_health##
## Fisher's exact test: p-value = 0.69
##
##
## ## Analysis for: animal_husbandry_one_health##
## Fisher's exact test: p-value = 0.46
##
##
## ## Analysis for: mental_health_one_health##
## Fisher's exact test: p-value = 0.53
##
##
## ## Analysis for: non_communicable_diseases_one_health##
## Fisher's exact test: p-value = 0.881
##
##
## ## Analysis for: animal_welfare_one_health##
## Fisher's exact test: p-value = 0.17
##
##
## ## Analysis for: climate_change_one_health##
## Fisher's exact test: p-value = 0.519
##
##
## ## Analysis for: plant_pests_one_health##
## Fisher's exact test: p-value = 0.683
- Observations
- Profession vs. One Health Knowledge Level
Cross-tabulation (Counts and Proportions):
Biomedical Scientist: 5 high (62.5%), 3 low (37.5%).
Medical Doctor: 4 high (100%), 0 low (0%).
Nurse: 106 high (78.5%), 29 low (21.5%).
Pharmacist: 5 high (100%), 0 low (0%).
High knowledge dominates, especially for Medical Doctors and Pharmacists (100%).
Fisher’s Exact Test: p = 0.357.
No significant association between profession and One Health knowledge level.
The high p-value suggests similar distributions of high/low knowledge across professions.
Visualization (Stacked Bar Plot): Medical Doctors and Pharmacists show 100% high knowledge.
Nurses and Biomedical Scientists have lower proportions of high knowledge (78.5% and 62.5%), with Biomedical Scientists showing the highest low knowledge proportion (37.5%).
The small sample sizes for Medical Doctors (4) and Pharmacists (5) limit reliability.
Observation: One Health knowledge is generally high (120/152 high, 78.9% from summary), but not significantly associated with profession.
Medical Doctors and Pharmacists appear to have perfect high knowledge, but small sample sizes reduce confidence.
- Profession vs. One Health Knowledge Scores
Cross-tabulation (Counts):
Biomedical Scientist: Scores range from 0 (1) to 14 (1), with peaks at 11–13.
Medical Doctor: Scores at 8 (1), 12 (1), 14 (2), all ≥8.
Nurse: Scores range from 0 (7) to 14 (6), with peaks at 9 (28) and 10 (19).
Pharmacist: Scores at 6 (1), 9 (1), 10 (2), 11 (1), mid-to-high range.
Nurses show the broadest distribution due to their large sample size.
Summary Statistics: Mean (SD, n): Biomedical Scientist: 9.25 (4.80, n=8).
Medical Doctor: 12.50 (2.38, n=4).
Nurse: 9.04 (3.43, n=135).
Pharmacist: 10.20 (1.92, n=5).
Medical Doctors have the highest mean score, followed by Pharmacists, while Nurses have the lowest.
ANOVA Test: F(3, 148) = 1.4448, p = 0.2321.
No significant differences in One Health knowledge scores across professions.
The moderate F-value suggests some variation, but the p-value indicates it’s not statistically significant.
Visualization (Boxplot): Medians: Medical Doctors highest (12), followed by Pharmacists (10), Biomedical Scientists (9–10), and Nurses (9).
Nurses have the widest spread (0–14), reflecting their sample size.
Overlapping distributions suggest no clear profession-based differences.
The boxplot with mean points (red diamonds) highlights Medical Doctors’ high mean (12.50).
Observation: One Health knowledge scores are strong (mean 9.18/14 from summary), with Medical Doctors scoring highest on average, but no significant differences by profession.
Small sample sizes for Medical Doctors (4) and Pharmacists (5) limit power to detect differences.
- Profession vs. Individual One Health Issues
Overview: 14 binary variables (Correct/Incorrect) were analyzed, each representing knowledge of a specific One Health issue ( one_health_definition, zoonosis one health,… ).
Fisher’s exact test was used for all due to low expected counts (< 5) in contingency tables, driven by small profession group sizes.
Significant Result:
Armed Conflict (armed_conflict_one_health): p = 0.006 (significant at α = 0.05).
Counts: Biomedical Scientist: 3 Correct (37.5%), 5 Incorrect (62.5%).
Medical Doctor: 4 Correct (100%), 0 Incorrect.
Nurse: 53 Correct (39.3%), 82 Incorrect (60.7%).
Pharmacist: 2 Correct (40%), 3 Incorrect (60%).
Proportions: Medical Doctors have 100% Correct Knowledge, while others are around 37.5–40% Correct.
Observation: Significant profession-based differences, with Medical Doctors showing perfect knowledge, possibly due to specialized education or exposure to conflict-related health issues.
Non-Significant Results (p-values from Fisher’s exact test): one_health_definition: p = 0.296.
biodiversity_one_health: p = 0.331.
deforestation_one_health: p = 0.673.
antibiotic_resistance_one_health: p = 0.678.
zoonosis_one_health: p = 0.363.
food_safety_one_health: p = 0.471.
urbanization_one_health: p = 0.69.
animal_husbandry_one_health: p = 0.466.
mental_health_one_health: p = 0.53.
non_communicable_diseases_one_health: p = 0.881.
animal_welfare_one_health: p = 0.17.
climate_change_one_health: p = 0.519.
plant_pests_one_health: p = 0.683.
No significant associations for these issues, with p-values indicating similar Correct/Incorrect knowledge distributions across professions.
Selected Issue Visualizations (Stacked Bar Plots for 4 issues): One Health Definition: Medical Doctors and Pharmacists: ~75–100% Correct.
Nurses and Biomedical Scientists: ~50% Correct.
No significant difference (p = 0.296).
Antibiotic Resistance: All professions: ~60–80% Correct, with Medical Doctors and Pharmacists slightly higher.
No significant difference (p = 0.678).
Climate Change: All professions: ~60–80% Correct, with Pharmacists at 100%.
No significant difference (p = 0.519).
Zoonosis: All professions: ~70–90% Correct, with Medical Doctors slightly lower.
No significant difference (p = 0.363).
Visualizations show high Correct knowledge proportions but no consistent profession-based patterns.
Observation: Only armed_conflict_one_health shows a significant association, driven by Medical Doctors’ perfect knowledge.
Other issues have high Correct knowledge ( zoonosis_one_health 74.4%, food_safety_one_health 76.3% from summary), but distributions are similar across professions.
##
## No Yes
## Biomedical Scientist 4 4
## Medical Doctor 3 1
## Nurse 89 46
## Pharmacist 5 0##
## No Yes
## Biomedical Scientist 50.00000 50.00000
## Medical Doctor 75.00000 25.00000
## Nurse 65.92593 34.07407
## Pharmacist 100.00000 0.00000##
## Fisher's Exact Test for Count Data
##
## data: prof_exp
## p-value = 0.3151
## alternative hypothesis: two.sided| No | Yes | No | Yes | |
|---|---|---|---|---|
| Biomedical Scientist | 4 | 4 | 50.00000 | 50.00000 |
| Medical Doctor | 3 | 1 | 75.00000 | 25.00000 |
| Nurse | 89 | 46 | 65.92593 | 34.07407 |
| Pharmacist | 5 | 0 | 100.00000 | 0.00000 |
| Note: | ||||
| Fisher’s exact test: p-value = 0.315 |
##
## 0 1
## Biomedical Scientist 6 2
## Medical Doctor 4 0
## Nurse 94 41
## Pharmacist 5 0##
## 0 1
## Biomedical Scientist 75.00000 25.00000
## Medical Doctor 100.00000 0.00000
## Nurse 69.62963 30.37037
## Pharmacist 100.00000 0.00000##
## Fisher's Exact Test for Count Data
##
## data: prof_rab_enc
## p-value = 0.3865
## alternative hypothesis: two.sided| 0 | 1 | 0 | 1 | |
|---|---|---|---|---|
| Biomedical Scientist | 6 | 2 | 75.00000 | 25.00000 |
| Medical Doctor | 4 | 0 | 100.00000 | 0.00000 |
| Nurse | 94 | 41 | 69.62963 | 30.37037 |
| Pharmacist | 5 | 0 | 100.00000 | 0.00000 |
| Note: | ||||
| Fisher’s exact test: p-value = 0.386 |
##
##
## # zoonosis_workshop Experience by Profession
##
##
## Table: zoonosis_workshop by Profession (Counts)
##
## | | No| Yes|
## |:--------------------|---:|---:|
## |Biomedical Scientist | 7| 1|
## |Medical Doctor | 3| 1|
## |Nurse | 126| 9|
## |Pharmacist | 5| 0|
##
##
## Table: zoonosis_workshop by Profession (%)
##
## | | No| Yes|
## |:--------------------|---------:|---------:|
## |Biomedical Scientist | 87.50000| 12.500000|
## |Medical Doctor | 75.00000| 25.000000|
## |Nurse | 93.33333| 6.666667|
## |Pharmacist | 100.00000| 0.000000|##
## Fisher's exact test p-value: 0.282
##
##
## # zoonosis_collaboration Experience by Profession
##
##
## Table: zoonosis_collaboration by Profession (Counts)
##
## | | No| Yes|
## |:--------------------|---:|---:|
## |Biomedical Scientist | 8| 0|
## |Medical Doctor | 4| 0|
## |Nurse | 126| 9|
## |Pharmacist | 5| 0|
##
##
## Table: zoonosis_collaboration by Profession (%)
##
## | | No| Yes|
## |:--------------------|---------:|--------:|
## |Biomedical Scientist | 100.00000| 0.000000|
## |Medical Doctor | 100.00000| 0.000000|
## |Nurse | 93.33333| 6.666667|
## |Pharmacist | 100.00000| 0.000000|##
## Fisher's exact test p-value: 1
##
##
## # zoonotic_disease_encounter Experience by Profession
##
##
## Table: zoonotic_disease_encounter by Profession (Counts)
##
## | | No| Yes|
## |:--------------------|--:|---:|
## |Biomedical Scientist | 4| 4|
## |Medical Doctor | 3| 1|
## |Nurse | 89| 46|
## |Pharmacist | 5| 0|
##
##
## Table: zoonotic_disease_encounter by Profession (%)
##
## | | No| Yes|
## |:--------------------|---------:|--------:|
## |Biomedical Scientist | 50.00000| 50.00000|
## |Medical Doctor | 75.00000| 25.00000|
## |Nurse | 65.92593| 34.07407|
## |Pharmacist | 100.00000| 0.00000|##
## Fisher's exact test p-value: 0.315
##
##
## # zoonosis_public_education Experience by Profession
##
##
## Table: zoonosis_public_education by Profession (Counts)
##
## | | No| Yes|
## |:--------------------|---:|---:|
## |Biomedical Scientist | 8| 0|
## |Medical Doctor | 4| 0|
## |Nurse | 121| 14|
## |Pharmacist | 5| 0|
##
##
## Table: zoonosis_public_education by Profession (%)
##
## | | No| Yes|
## |:--------------------|---------:|--------:|
## |Biomedical Scientist | 100.00000| 0.00000|
## |Medical Doctor | 100.00000| 0.00000|
## |Nurse | 89.62963| 10.37037|
## |Pharmacist | 100.00000| 0.00000|##
## Fisher's exact test p-value: 1
##
##
## # one_health_training Experience by Profession
##
##
## Table: one_health_training by Profession (Counts)
##
## | | No| Yes|
## |:--------------------|---:|---:|
## |Biomedical Scientist | 4| 4|
## |Medical Doctor | 4| 0|
## |Nurse | 107| 28|
## |Pharmacist | 5| 0|
##
##
## Table: one_health_training by Profession (%)
##
## | | No| Yes|
## |:--------------------|---------:|--------:|
## |Biomedical Scientist | 50.00000| 50.00000|
## |Medical Doctor | 100.00000| 0.00000|
## |Nurse | 79.25926| 20.74074|
## |Pharmacist | 100.00000| 0.00000|##
## Fisher's exact test p-value: 0.105
##
##
## # zoonosis_training Experience by Profession
##
##
## Table: zoonosis_training by Profession (Counts)
##
## | | No| Yes|
## |:--------------------|--:|---:|
## |Biomedical Scientist | 1| 7|
## |Medical Doctor | 3| 1|
## |Nurse | 89| 46|
## |Pharmacist | 5| 0|
##
##
## Table: zoonosis_training by Profession (%)
##
## | | No| Yes|
## |:--------------------|---------:|--------:|
## |Biomedical Scientist | 12.50000| 87.50000|
## |Medical Doctor | 75.00000| 25.00000|
## |Nurse | 65.92593| 34.07407|
## |Pharmacist | 100.00000| 0.00000|##
## Fisher's exact test p-value: 0.004
- Observations
- Profession vs. Experience with Zoonotic Diseases
Cross-tabulation (Counts and Proportions):
Biomedical Scientist: 4 No (50%), 4 Yes (50%).
Medical Doctor: 3 No (75%), 1 Yes (25%).
Nurse: 89 No (65.9%), 46 Yes (34.1%).
Pharmacist: 5 No (100%), 0 Yes (0%).
Biomedical Scientists have the highest proportion of experience (50%), followed by Nurses (34.1%). Pharmacists report no encounters.
Fisher’s Exact Test: p = 0.3151.
No significant association between profession and zoonotic disease encounters.
The high p-value suggests similar encounter rates across professions.
Visualization (Stacked Bar Plot): Biomedical Scientists show an even split (50% Yes/No).
Nurses have ~34% Yes, Medical Doctors ~25% Yes, and Pharmacists 0% Yes.
Observation: Overall, 33.6% (51/152) report encounters (from summary), with Biomedical Scientists and Nurses most likely to have experience.
No significant profession-based differences, likely due to small non-Nurse sample sizes and low power.
- Profession vs. Rabies Encounters
Cross-tabulation (Counts and Proportions):
Biomedical Scientist: 6 No (75%), 2 Yes (25%).
Medical Doctor: 4 No (100%), 0 Yes (0%).
Nurse: 94 No (69.6%), 41 Yes (30.4%).
Pharmacist: 5 No (100%), 0 Yes (0%).
Nurses have the highest proportion of rabies encounters (30.4%), followed by Biomedical Scientists (25%).
Fisher’s Exact Test: p = 0.3865.
No significant association between profession and rabies encounters.
The high p-value indicates similar encounter rates across professions.
Visualization (Stacked Bar Plot): Nurses show ~30% Yes, Biomedical Scientists ~25% Yes.
Medical Doctors and Pharmacists have 0% Yes, showing no rabies encounters.
Observation: Rabies is the most encountered zoonotic disease (28.3%, 43/152 from summary), primarily among Nurses.
No significant differences by profession, possibly due to sparse data (e.g., 0 encounters for Medical Doctors and Pharmacists).
- Profession vs. Experience with Zoonoses and One Health (Six Variables) Overview:
Six binary variables were analyzed, each representing a type of experience or training.
Fisher’s exact test was used for all due to low expected counts (< 5), driven by small profession group sizes and low “Yes” responses.
Zoonosis Workshop (zoonosis_workshop): Counts and Proportions: Biomedical Scientist: 7 No (87.5%), 1 Yes (12.5%).
Medical Doctor: 3 No (75%), 1 Yes (25%).
Nurse: 126 No (93.3%), 9 Yes (6.7%).
Pharmacist: 5 No (100%), 0 Yes (0%).
Medical Doctors have the highest participation rate (25%), but overall participation is low (7.2%, 11/152).
Fisher’s Exact Test: p = 0.282.
Observation: No significant association. Low participation limits analysis power.
Zoonosis Collaboration (zoonosis_collaboration): Counts and Proportions: Biomedical Scientist: 8 No (100%), 0 Yes.
Medical Doctor: 4 No (100%), 0 Yes.
Nurse: 126 No (93.3%), 9 Yes (6.7%).
Pharmacist: 5 No (100%), 0 Yes.
Only Nurses report collaboration (all Rabies-related, per summary).
Fisher’s Exact Test: p = 1.0.
Observation: No significant association. Sparse “Yes” responses (5.9%, 9/152) and zero responses for non-Nurses block meaningful analysis.
Zoonosis Public Education:
Counts and Proportions: Biomedical Scientist: 8 No (100%), 0 Yes.
Medical Doctor: 4 No (100%), 0 Yes.
Nurse: 121 No (89.6%), 14 Yes (10.4%).
Pharmacist: 5 No (100%), 0 Yes.
Only Nurses report public education (mostly Rabies, per summary).
Fisher’s Exact Test: p = 1.0.
Observation: No significant association. Low engagement (9.2%, 14/152) and zero responses for non-Nurses limit analysis.
One Health Training:
Counts and Proportions:
Biomedical Scientist: 4 No (50%), 4 Yes (50%).
Medical Doctor: 4 No (100%), 0 Yes.
Nurse: 107 No (79.3%), 28 Yes (20.7%).
Pharmacist: 5 No (100%), 0 Yes.
Biomedical Scientists have the highest training rate (50%).
Fisher’s Exact Test: p = 0.105.
Observation: No significant association, but Biomedical Scientists’ high training rate is notable. Low overall training (21.1%, 32/152) and small sample sizes limit power.
Zoonosis Training:
Counts and Proportions:
Biomedical Scientist: 1 No (12.5%), 7 Yes (87.5%).
Medical Doctor: 3 No (75%), 1 Yes (25%).
Nurse: 89 No (65.9%), 46 Yes (34.1%).
Pharmacist: 5 No (100%), 0 Yes.
Biomedical Scientists have the highest training rate (87.5%), followed by Nurses (34.1%).
Fisher’s Exact Test: p = 0.004.
Observation: significant association, Biomedical Scientists have the highest training rate (87.5%)
##
## No Yes
## Biomedical Scientist 8 0
## Medical Doctor 4 0
## Nurse 126 9
## Pharmacist 5 0##
## No Yes
## Biomedical Scientist 100.000000 0.000000
## Medical Doctor 100.000000 0.000000
## Nurse 93.333333 6.666667
## Pharmacist 100.000000 0.000000##
## Fisher's Exact Test for Count Data
##
## data: prof_collab
## p-value = 1
## alternative hypothesis: two.sided| No | Yes | No | Yes | |
|---|---|---|---|---|
| Biomedical Scientist | 8 | 0 | 100.00000 | 0.000000 |
| Medical Doctor | 4 | 0 | 100.00000 | 0.000000 |
| Nurse | 126 | 9 | 93.33333 | 6.666667 |
| Pharmacist | 5 | 0 | 100.00000 | 0.000000 |
| Note: | ||||
| Fisher’s exact test: p-value = 1 |
##
## No Yes
## Biomedical Scientist 8 0
## Medical Doctor 4 0
## Nurse 121 14
## Pharmacist 5 0##
## No Yes
## Biomedical Scientist 100.00000 0.00000
## Medical Doctor 100.00000 0.00000
## Nurse 89.62963 10.37037
## Pharmacist 100.00000 0.00000##
## Fisher's Exact Test for Count Data
##
## data: prof_edu
## p-value = 1
## alternative hypothesis: two.sided| No | Yes | No | Yes | |
|---|---|---|---|---|
| Biomedical Scientist | 8 | 0 | 100.00000 | 0.00000 |
| Medical Doctor | 4 | 0 | 100.00000 | 0.00000 |
| Nurse | 121 | 14 | 89.62963 | 10.37037 |
| Pharmacist | 5 | 0 | 100.00000 | 0.00000 |
| Note: | ||||
| Fisher’s exact test: p-value = 1 |
##
##
## # Collaboration on anthrax
##
##
## Table: Test for collab_anthrax
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|
##
##
## # Collaboration on rabies
##
##
## Table: Test for collab_rabies
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | 1.2| 3| 0.754|
##
##
## # Collaboration on brucellosis
##
##
## Table: Test for collab_brucellosis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|
##
##
## # Collaboration on trypanosomiasis
##
##
## Table: Test for collab_trypanosomiasis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|
##
##
## # Collaboration on tuberculosis
##
##
## Table: Test for collab_tuberculosis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|
##
##
## # Encounters with anthrax
##
##
## Table: Test for encounter_anthrax
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | 0.25| 3| 0.969|
##
##
## # Encounters with rabies
##
##
## Table: Test for encounter_rabies
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | 3.86| 3| 0.277|
##
##
## # Encounters with brucellosis
##
##
## Table: Test for encounter_brucellosis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|
##
##
## # Encounters with trypanosomiasis
##
##
## Table: Test for encounter_trypanosomiasis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | 18| 3| 0|
##
##
## # Encounters with tuberculosis
##
##
## Table: Test for encounter_tuberculosis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | 16.6| 3| 0.001|Collaboration Summary: Mean collaboration rates for collab_anthrax, collab_rabies, visualized as a dodged bar plot.
Encounter Summary: Mean encounter rates for encounter_anthrax, encounter_rabies, etc., visualized as a heatmap.
Kruskal-Wallis Tests: Non-parametric tests for collaboration and encounter variables.
Collaboration Summary and Visualization Summary: Variables: collab_anthrax, collab_rabies, collab_brucellosis, collab_trypanosomiasis, collab_tuberculosis.
Only collab_rabies has non-zero responses (mean = 0.067 for Nurses, 0 for others).
Others are all 0 (no collaboration).
Visualization (Dodged Bar Plot):
Only Nurses show non-zero mean collaboration (~0.067 for Rabies).
All other professions and diseases show zero collaboration.
Plot highlights Nurses’ exclusive but low collaboration on Rabies.
Kruskal-Wallis Tests: collab_rabies: ChiSq = 1.2, df = 3, p = 0.754 (not significant).
Others (collab_anthrax, collab_brucellosis, collab_trypanosomiasis, collab_tuberculosis): NaN (no variation, all 0).
No significant differences, consistent with Fisher’s test (p = 1.0).
Observation: Collaboration is limited to Rabies and Nurses, with no profession-based differences.
Encounter Summary and Visualization Summary: Variables: encounter_anthrax (2 Yes), encounter_rabies (43 Yes), encounter_brucellosis (0 Yes), encounter_trypanosomiasis (1 Yes), encounter_tuberculosis (3 Yes).
Mean encounter rates: Nurses: Highest for Rabies (0.30), low for Anthrax (0.015), Trypanosomiasis (0.007), Tuberculosis (0.022).
Biomedical Scientists: Rabies (~0.25), others 0.
Medical Doctors, Pharmacists: All 0.
Visualization (Heatmap): Nurses show the highest encounter rates (red for Rabies, faint red for others).
Biomedical Scientists show moderate Rabies encounters (light red).
Medical Doctors and Pharmacists are white (no encounters).
Rabies dominates encounters, with minimal activity for other diseases.
Kruskal-Wallis Tests: encounter_anthrax: ChiSq = 0.25, df = 3, p = 0.969 (not significant).
encounter_rabies: ChiSq = 3.86, df = 3, p = 0.277 (not significant).
encounter_brucellosis: NaN (no variation, all 0).
encounter_trypanosomiasis: ChiSq = 18, df = 3, p = 0.000 (significant).
encounter_tuberculosis: ChiSq = 16.6, df = 3, p = 0.001 (significant).
Observation: Significant differences for encounter_trypanosomiasis and encounter_tuberculosis, driven by Nurses’ exclusive encounters (1 and 3 Yes, respectively).
No significant differences for encounter_rabies or encounter_anthrax, consistent with Fisher’s tests (p = 0.386 and p ≈ 1.0).
Sparse data (1–3 Yes for non-Rabies diseases) and zero responses for non-Nurses limit reliability.
- Key trends so far
Knowledge: Strong for prioritized zoonoses (87.5% high) and One Health (78.9% high), moderate for general zoonosis (46.1% high). Medical Doctors and Biomedical Scientists often score higher, but small sample sizes limit significance.
Experience: Low engagement in workshops (7.2%), collaboration (5.9%), and public education (9.2%). Biomedical Scientists lead in training, Nurses in encounters.
Significant Differences: Found in Brucellosis/Trypanosomiasis knowledge, armed conflict knowledge, zoonosis training, and Trypanosomiasis/Tuberculosis encounters, favoring Medical Doctors, Biomedical Scientists, or Nurses.
Nurse Dominance: Nurses drive most outcomes due to sample size (135/152).
Pharmacists: Minimal involvement across all outcomes.
##
##
## # Education on anthrax
##
##
## Table: Test for educate_anthrax
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|
##
##
## # Education on rabies
##
##
## Table: Test for educate_rabies
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | 1.78| 3| 0.62|
##
##
## # Education on brucellosis
##
##
## Table: Test for educate_brucellosis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|
##
##
## # Education on trypanosomiasis
##
##
## Table: Test for educate_trypanosomiasis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|
##
##
## # Education on tuberculosis
##
##
## Table: Test for educate_tuberculosis
##
## | |Test | ChiSq| df| p.value|
## |:--------------------------|:--------------|-----:|--:|-------:|
## |Kruskal-Wallis chi-squared |Kruskal-Wallis | NaN| 3| NaN|| No | Yes | |
|---|---|---|
| Biomedical Scientist | 8 | 0 |
| Medical Doctor | 4 | 0 |
| Nurse | 122 | 13 |
| Pharmacist | 5 | 0 |
##
## Fisher's Exact Test for Count Data
##
## data: table_test
## p-value = 1
## alternative hypothesis: two.sided
| No | Yes | |
|---|---|---|
| Biomedical Scientist | 8 | 0 |
| Medical Doctor | 4 | 0 |
| Nurse | 126 | 9 |
| Pharmacist | 5 | 0 |
##
## Fisher's Exact Test for Count Data
##
## data: table_test
## p-value = 1
## alternative hypothesis: two.sided
- Observations
Only educate_rabies has non-zero responses (13 Yes, mean = 0.096 for Nurses, 0 for others).
Others are all 0 (no education reported).
Visualization (Dodged Bar Plot): Only Nurses show non-zero mean education coverage (~0.096 for Rabies).
All other professions and diseases show zero coverage.
Plot highlights Nurses’ exclusive but low engagement in Rabies education.
Kruskal-Wallis Tests: educate_rabies: ChiSq = 1.78, df = 3, p = 0.62 (not significant).
Others (educate_anthrax, educate_brucellosis, educate_trypanosomiasis, educate_tuberculosis): NaN (no variation, all 0).
No significant differences, consistent with Fisher’s test for educate_rabies (p = 1.0).
Observation: Public education is limited to Rabies and Nurses (8.6% Yes).
No profession-based differences due to sparse data (13 Yes for Rabies, 0 for others).
Conclusion
- Knowledge of Zoonoses and One Health
General Zoonosis Knowledge :
Moderate knowledge (mean 1.53/3, 46.1% high).
No significant differences by profession (ANOVA p = 0.869, Fisher’s p = 0.965).
Medical Doctors and Biomedical Scientists have slightly higher medians, but small sample sizes (n=4, n=8) limit power.
Prioritized Zoonoses Knowledge: Strong knowledge (mean 6.53/9, 87.5% high).
No significant differences (ANOVA p = 0.246, Fisher’s p = 0.793).
Medical Doctors score highest (median ~8), but n=4.
Five Selected Zoonoses Knowledge:
Significant differences for brucellosis_knowledge_total (p = 0.0318) and trypanosomiasis_knowledge_total (p = 0.0107).
Rabies knowledge highest (mean 4.02/8), Brucellosis lowest (2.06/7).
Medical Doctors and Biomedical Scientists score higher for Brucellosis/Trypanosomiasis
Categorical tests infeasible due to invariant responses (all inadequate except Anthrax/Rabies).
One Health Knowledge:
Strong knowledge (mean 9.18/14, 78.9% high).
Significant for armed_conflict_one_health (p = 0.006), with Medical Doctors at 100% correct.
No significant differences overall (ANOVA p = 0.232, Fisher’s p = 0.357).
Medical Doctors (mean 12.50) and Pharmacists (100% high) excel, but n=4–5.
- Experience with Zoonoses and One Health
General Experience:
Significant for zoonosis_training (p = 0.004), with Biomedical Scientists at 87.5% Yes
Other variables (zoonosis_workshop, zoonosis_collaboration, zoonotic_disease_encounter, zoonosis_public_education, one_health_training) not significant (p = 0.105–1.0).
Engagement low: workshops (7.2%), collaboration (5.9%), encounters (33.6%), public education (9.2%), One Health training (21.1%), zoonosis training (35.5%).
Biomedical Scientists lead in training (50–87.5% Yes), Nurses in encounters (34.1% Yes).
Zoonoses Controlled in Collaboration:
No significant differences for collab_rabies (Fisher’s p = 1.0, Kruskal-Wallis p = 0.754).
Rare (5.9% Yes), Nurse-only, all Rabies. Other diseases (Anthrax, Brucellosis, Trypanosomiasis, Tuberculosis) have zero collaboration.
Zoonoses Encountered :
Significant for encounter_trypanosomiasis (p = 0.000) and encounter_tuberculosis (p = 0.001), Nurse-only (1 and 3 Yes).
Not significant for encounter_rabies (p = 0.277) or encounter_anthrax (p = 0.969); encounter_brucellosis is zero.
Rabies most encountered (28.3%), others rare (0.7–2.0%). Nurses dominate, Biomedical Scientists have some Rabies encounters (25%).
Zoonoses Covered in Public Health Education :
No significant differences for educate_rabies (Fisher’s p = 1.0, Kruskal-Wallis p = 0.62).
Rare (8.6% Yes), Nurse-only, Rabies-only. Other diseases (Anthrax, Brucellosis, Trypanosomiasis, Tuberculosis) have zero education.
- Significant Findings
Knowledge:
brucellosis_knowledge_total: Medical Doctors and Biomedical Scientists > Nurses (p = 0.0318).
trypanosomiasis_knowledge_total: Medical Doctors and Biomedical Scientists > Nurses (p = 0.0107).
armed_conflict_one_health: Medical Doctors > Nurses (p = 0.006).
Experience:
zoonosis_training: Biomedical Scientists > Nurses (p = 0.004).
encounter_trypanosomiasis and encounter_tuberculosis: Nurses > Others (p = 0.000, 0.001).
- Key trends
Knowledge Strengths: Prioritized zoonoses (87.5% high) and One Health (78.9% high) are strong, general zoonosis moderate (46.1% high). Rabies knowledge is highest, Brucellosis lowest.
Profession-Based Patterns: Medical Doctors: Highest knowledge scores (e.g., One Health mean 12.50, Brucellosis), perfect armed conflict knowledge, but low engagement (0–25% Yes) due to n=4.
Biomedical Scientists: Excel in training (87.5% zoonosis, 50% One Health), strong Brucellosis/Trypanosomiasis knowledge, some Rabies encounters (25%).
Nurses: Dominate encounters (34.1%), collaboration (6.7%), and education (9.6%) due to sample size (n=135), but moderate knowledge/training.
Pharmacists: Minimal involvement (0% Yes for most binary outcomes, 100% high One Health knowledge, but n=5).
Sparse Engagement: Collaboration, public education, and non-Rabies encounters are rare (0.7–9.2% Yes), mostly Nurse-driven.
Rabies Dominance: Most encountered (28.3%), collaborated (5.9%), and educated (8.6%) zoonosis.
- Visualizations
Stacked Bar Plots: Highlight Nurse-dominated, low “Yes” proportions for collaboration/education.
Boxplots: Show Medical Doctors’ high knowledge medians, Nurses’ wide spread.
Dodged Bar Plots: Confirm Nurses’ exclusive Rabies collaboration/education.
Heatmap: Visualizes Nurses’ dominance in Rabies encounters, sparse non-Rabies encounters.
- Limitations Profession Imbalance: Nurses (88.8%, 135/152) dominate, with small samples for Medical Doctors (4) and Pharmacists (5), reducing statistical power.
Sparse data
- Summary
The analyses reveal strong knowledge of prioritized zoonoses and One Health among rural health professionals, with moderate general zoonosis knowledge. Significant profession-based differences are limited but notable: Medical Doctors and Biomedical Scientists excel in Brucellosis and Trypanosomiasis knowledge, Medical Doctors in armed conflict knowledge, Biomedical Scientists in zoonosis training, and Nurses in Trypanosomiasis and Tuberculosis encounters. Engagement in workshops, collaboration, and public education is low, primarily Nurse-driven, and focused on Rabies. Nurses dominate outcomes due to their large sample size, while Medical Doctors show high knowledge but low engagement, and Pharmacists are minimally involved. Small non-Nurse sample sizes, sparse data, and unadjusted confounders (age, location) limit the detection of differences. These findings suggest targeted training for Nurses in specific zoonoses (e.g., Brucellosis) and increased engagement opportunities for Medical Doctors and Pharmacists in zoonotic disease control. Thank you